Semi-online parameter identification method, system, device and storage medium for lithium battery

By employing a semi-online parameter identification method, combined with an adaptive cooperative differential evolution algorithm and a dynamic allocation strategy for computing resources, the problem of low accuracy in lithium battery parameter identification algorithms is solved. This achieves globally optimal parameter identification, improving the accuracy of lithium battery parameter identification and the real-time performance of SOC estimation.

CN116068430BActive Publication Date: 2026-06-26WUHAN UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
WUHAN UNIV OF TECH
Filing Date
2022-12-15
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing lithium battery parameter identification algorithms are not very accurate, and the identification results are not globally optimal, which affects the accuracy of lithium battery parameter identification.

Method used

A semi-online parameter identification method is adopted to establish a first-order equivalent circuit model of lithium battery. Identification vectors are constructed through segmented identification and data forgetting mechanism. Combined with adaptive cooperative differential evolution algorithm and dynamic allocation strategy of computing resources, lithium battery parameters are optimized and the global optimal solution is output.

Benefits of technology

It improves the accuracy of lithium battery parameter identification, reduces the computational burden on the battery energy management system, provides real-time parameter identification reference, and enhances the accuracy of SOC estimation.

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Abstract

The application provides a lithium battery semi-online parameter identification method, system, device and storage medium, the method comprises the following steps: establishing a first-order equivalent circuit model of a lithium battery and determining the type of parameters to be measured; initializing the sampling parameters, collecting voltage and current data during the charging / discharging process of the lithium battery in the semi-online identification period; constructing an identification vector and a target function in the semi-online identification period based on the collected voltage and current data; performing iterative optimization operation on the identification vector based on the adaptive collaborative differential evolution algorithm and the dynamic allocation strategy of computing resources; determining whether the current charging / discharging process of the lithium battery is completed, and outputting the global optimal solution parameters corresponding to the lithium battery in the semi-online identification period according to the determination result. The semi-online parameter identification method for the lithium battery provided by the application can comprehensively improve the accuracy of lithium battery parameter identification.
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